Variational data assimilation for morphodynamic model parameter

estimationDepartment of Mathematics, University of Reading:

Polly Smith*, Sarah Dance, Mike Baines, Nancy Nichols,

Environmental Systems Science Centre, University of Reading:Tania Scott

*email: p.j.smith@reading.ac.uk

This project is funded under the Natural Environment Research Council (NERC) Flood Risk From Extreme Events (FREE) programme, with additional funding provided by the Environment Agency as part of the CASE (Co-operative Awards in Science and Engineering) scheme. Thanks also to HR Wallingford for visits and useful discussions.

Outline• Background/ motivation

- what is morphodynamic modelling?

- why do we need morphodynamic models?

• A simple 1D morphodynamic model

• Data assimilation and parameter estimation- how can we use data assimilation to estimate uncertain model

parameters?

- how do we model the background error covariances?

• Results

• Summary

Terminology• Bathymetry - the underwater equivalent to topography

- coastal bathymetry is dynamic and evolves with time- water action erodes, transports, and deposits sediment, which changes

the bathymetry, which alters the water action, and so on

• Morphodynamics - the study of the evolution of the bathymetry in response to the flow induced sediment transport

• Morphodynamic prediction

- why?- how?

Kent channel

• Channel movement– impacts on habitats in the bay– affects access to ports– has implications for flooding

during storm events

1960s

1970s

1997

18km

Picture courtesy of Nigel Cross, Lancaster City Council

Morphodynamic modelling

• Operational coastal flood forecasting is limited near-shore by lack of knowledge of evolving bathymetry

– but it is impractical to continually monitor large coastal areas

• Modelling is difficult– longer term changes are driven by shorter term processes – uncertainty in initial conditions and parameters

• An alternative approach is to use data assimilation

Parameter estimation• Model equations depend on parameters

- exact values are unknown- inaccurate parameter values can lead to growth of model error- affects predictive ability of the model

• How do we estimate these values a priori?- theoretical values- calibration

or ...• data assimilation

- choose parameters based on observations- state augmentation: model parameters are estimated alongside

the model state

Simple 1D modelBased on the sediment conservation equation

where z(x,t) is the bathymetry, t is time, q is the sediment transport rate in the x direction and is the sediment porosity.

For the sediment transport rate we use the power law

where u(x,t) is the depth averaged current and A and n are parameters whose values need to be set

If we assume that water flux (F) and height (H) are constant

we can rewrite the sediment conservation equation as

where a(z, H, F,ε,A,n) is the advection velocity or bed celerity.

Can we use data assimilation to estimate the parameters A and n?

model run with incorrect parameters & without data assimilation

red line = correct parametersblue line = incorrect parameters (A over estimated, n under estimated)

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State augmentation• Dynamical system model

(discrete, non-linear, time invariant)

• Parameter evolution

• Augmented system model

• Observations

in terms of the augmented system ...

where

3D Var Cost function:

B̃ and R are the covariance matrices of the background and observation errors.

Bzz state background error covariance

Bpp parameter background error covariance

Bzp state parameter error cross covariance

augmented gain matrix:

state & parameter updates:

State-parameter cross covariancesThe Extended Kalman filter (EKF)

State forecast:

Error covariance forecast:

where

Error covariance forecast:

a new hybrid approach ...

for our simple 2 parameter model

Model setup• Assume perfect model and observations• Identical twin experiments

- reference solution generated using Gaussian initial data and parameter values A = 0.002 ms-1 and n = 3.4

• Use incorrect model inputs- inaccurate initial bathymetry- inaccurate parameter estimates

• 3D Var algorithm is applied sequentially- observations taken at fixed grid points & assimilated every hour- the cost function is minimized iteratively using a quasi-Newton

descent algorithm

• Covariances- Bzz fixed

- Bzp time varying

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with data assimilation …

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without data assimilation

with data assimilation …

Summary• Presented a novel approach to model parameter

estimation using data assimilation- demonstrated the technique using a simple morphodynamic model

• Results are very encouraging- scheme is capable of recovering near-perfect parameter values

- improves model performance

• What next …?- can our scheme be successfully applied to more complex models?

- can we say anything about the convergence of the system?

Questions?

Simple Models of Changing Bathymetry with Data Assimilation

P.J Smith, M.J. Baines, S.L. Dance, N.K. Nichols and T.R. ScottDepartment of Mathematics, University of Reading

Numerical Analysis Report 10/2007*

Data Assimilation for Parameter Estimation with Application to a Simple Morphodynamic Model

P.J Smith, M.J. Baines, S.L. Dance, N.K. Nichols and T.R. ScottDepartment of Mathematics, University of Reading

Mathematics Report 2/2008*

Variational data assimilation for parameter estimation: application to a simple morphodynamic model

P.J Smith, M.J. Baines, S.L. Dance, N.K. Nichols and T.R. ScottSubmitted to Ocean Dynamics PECS 2008 Special Issue*

*available from http://www.reading.ac.uk/maths/research/